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On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature

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Abstract

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia; it has a high socioeconomic impact in Western countries. Therefore, it is one of the most active research areas today. Alzheimer’s disease is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a postmortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early AD detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of AD by noninvasive methods. The purpose is to examine, in a pilot study, the potential of applying machine learning algorithms to speech features obtained from suspected Alzheimer’s disease sufferers in order to help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: spontaneous speech and emotional response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of AD patients.

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Acknowledgments

This work has been partially supported by a SAIOTEK grant from the Basque Government, the University of Vic under the research Grant R0904, and the Spanish Ministerio de Ciencia e Innovación TEC2012-38630-C04-03. Professor Iciar Martinez (Research Center for Experimental Marine Biology and Biotechnology-Plentziako Itsas Estazioa (PIE), University of the Basque Country and IKERBASQUE, Basque Foundation for Science) is gratefully acknowledged for helpful discussions and for her contribution to the preparation of the manuscript.

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Authors and Affiliations

  1. Systems Engineering and Automation Department, University of the Basque Country, 20008, Donostia, Spain

    K. López-de-Ipiña, N. Barroso & H. Eguiraun

  2. IDeTIC, Universidad de Las Palmas de Gran Canaria, Las Palmas, Spain

    J. B. Alonso, P. Henriquez & C. M. Travieso

  3. Digital Technologies Group, University of Vic, Catalonia, Spain

    J. Solé-Casals

  4. Escola Universitària Politècnica de Mataró (UPC), Tecnocampus, Barcelona, Spain

    M. Faundez-Zanuy

  5. CITA-Alzheimer Foundation, Donostia, Spain

    M. Ecay-Torres & P. Martínez-Lage

  6. Research Center for Experimental Marine Biology and Biotechnology, Plentzia Marine Station, University of the Basque Country, Plentzia, Spain

    H. Eguiraun

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  1. K. López-de-Ipiña
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  2. J. B. Alonso
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  3. J. Solé-Casals
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  4. N. Barroso
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  6. M. Faundez-Zanuy
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  7. C. M. Travieso
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  8. M. Ecay-Torres
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Correspondence to K. López-de-Ipiña.

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López-de-Ipiña, K., Alonso, J.B., Solé-Casals, J. et al. On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature. Cogn Comput 7, 44–55 (2015). https://doi.org/10.1007/s12559-013-9229-9

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  • DOI: https://doi.org/10.1007/s12559-013-9229-9

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